Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E

Vecchio, Fabrizio; Miraglia, Francesca; Iberite, Francesco; Lacidogna, Giordano; Guglielmi, Valeria; Marra, Camillo; Pasqualetti, Patrizio; Tiziano, Francesco Danilo; Rossini, Paolo Maria

doi:10.1002/ana.25289

Cited by 89Dementia and Cognitive Impairment ResearchEEG and Brain-Computer InterfacesFunctional Brain Connectivity Studies

Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E

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Vecchio, Fabrizio, Miraglia, Francesca, Iberite, Francesco, Lacidogna, Giordano, Guglielmi, Valeria, Marra, Camillo, Pasqualetti, Patrizio, Tiziano, Francesco Danilo +1 more·2018·Annals of Neurology

DOI 10.1002/ana.25289

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Abstract

Objective: Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic‐MCI (aMCI) subjects progress to dementia (typically to Alzheimer‐Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo‐E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year. Methods: 145 aMCI subjects were divided into two sub‐groups and, according to the clinical follow‐up, were classified as Converted to AD (C‐MCI, 71) or Stable (S‐MCI, 74). Results: Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C‐MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first‐order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo‐E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%). Interpretation: Concluding, this innovative EEG analysis, in combination with a genetic test (both low‐cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302–314

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Few studies have evaluated combinations of multiple markers within the same study population.
“Few studies have evaluated combinations of multiple markers within the same study population. 17”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [17]
The Discussion (page unavailable) says this specific combination of EEG graph theory and ApoE genotyping had not previously been reported for predicting conversion from MCI to Alzheimer dementia, although it had been used for diagnosis. The paper also evaluates those markers in the same participants. It does not establish the broader claim that few studies have evaluated combinations of multiple markers within one population. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Modern software for EEG signal analysis can quantify on an individual basis slowing of brain EEG rhythms in AD and MCI subjects, and measure abnormalities of synchronization mechanisms of rhythmic oscillations of cortical pyramidal neuronal assemblies, which are at the basis of defective information transfer across brain regions.
“modern software for EEG signal analysis can quantify on an individual basis slowing of brain EEG rhythms in AD and MCI subjects, and measure abnormalities of synchronization mechanisms of rhythmic oscillations (EEG/MEG frequency bands) of cortical pyramidal neuronal assemblies, which are at the basis of defective information transfer across brain regions 17 , 29 , 44 , 45 , 46”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [17]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
When graph theory analysis of EEG signals is combined with APOE genotyping, an extremely high level of accuracy in identification of Alzheimer's disease from cognitively unimpaired elderly as well as MCI-prodromal-to-AD cases can be reached.
“When graph theory analysis of EEG signals is combined with APOE genotyping an extremely high level of accuracy in identification of AD from cognitively unimpaired elderly as well as MCI‐prodromal‐to‐AD cases can be reached. 17”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [17]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
EEG markers can distinguish early in their clinical course prodromal-to-AD MCI subjects after about 1 year from baseline EEG recordings and clinical demonstration of MCI.
“It has been demonstrated that EEG markers can distinguish early in their clinical course prodromal‐to‐AD MCI subjects after about 1 year from baseline EEG recordings and clinical demonstration of MCI. 16 , 17 , 58 , 59 , 60”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [17]

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Small World Index in Default Mode Network Predicts Progression from Mild Cognitive Impairment to Dementia
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Resting-state electroencephalographic biomarkers of Alzheimer’s disease
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Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia
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Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
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The road ahead in clinical network neuroscience
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Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau
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Abnormal cortical neural synchronization mechanisms in quiet wakefulness are related to motor deficits, cognitive symptoms, and visual hallucinations in Parkinson's disease patients: an electroencephalographic study
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Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer’s Disease Versus Vascular Dementia Diagnosis
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Theta Band-Power Shapes Amyloid-Driven Longitudinal EEG Changes in Elderly Subjective Memory Complainers At-Risk for Alzheimer’s Disease
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Information Contained in EEG Allows Characterization of Cognitive Decline in Neurodegenerative Disorders
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Plasma BDNF Levels Following Transcranial Direct Current Stimulation Allow Prediction of Synaptic Plasticity and Memory Deficits in 3×Tg-AD Mice
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Contrasting Metabolic Insufficiency in Aging and Dementia
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Comparing machine learning‐derived MRI‐based and blood‐based neurodegeneration biomarkers in predicting syndromal conversion in early AD
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Human Brain Networks in Physiological and Pathological Aging: Reproducibility of Electroencephalogram Graph Theoretical Analysis in Cortical Connectivity
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The brain network organization during sleep onset after deprivation
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Assessing the dependence of the number of EEG channels in the brain networks’ modulations
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Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
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Dynamics of the “Cognitive” Brain Wave P3b at Rest for Alzheimer Dementia Prediction in Mild Cognitive Impairment
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Clinical and Instrumental Characterization of Patients With Late-Onset Epilepsy
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Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects
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Coupled sleep rhythm disruption predicts cognitive decline in Alzheimer’s disease
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The electrophysiological and neuropathological profiles of cerebellum in APPswe/PS1ΔE9 mice: A hypothesis on the role of cerebellum in Alzheimer's disease
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Task aftereffect reorganization of resting state functional brain networks in healthy aging and mild cognitive impairment
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Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer’s disease: protocol for a rapid systematic review
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The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition
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Timely Detection of Mild Cognitive Impairment in Italy: An Expert Opinion
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Electroencephalographic hallmarks of Alzheimer’s disease
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Small‐world network and neuroscience
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Neural decoding reliability: Breakthroughs and potential of brain–computer interfaces technologies in the treatment of neurological diseases
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Genetic Association Between Alzheimer’s Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-demented Adults
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Modern neurophysiological techniques indexing normal or abnormal brain aging
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Cognitive Decline Risk Stratification in People with Late-Onset Epilepsy of Unknown Etiology: An Electroencephalographic Connectivity and Graph Theory Pilot Study
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Abnormalities of Cortical Sources of Resting State Delta Electroencephalographic Rhythms Are Related to Epileptiform Activity in Patients With Amnesic Mild Cognitive Impairment Not Due to Alzheimer's Disease
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Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach
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Aging and brain connectivity by graph theory
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Exploring the disruptions of the neurophysiological organization in Alzheimer’s disease: An integrative approach
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Non-invasive brain stimulation in cognitive sciences and Alzheimer's disease
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CVN‐AD Alzheimer's mice show premature reduction in neurovascular coupling in response to spreading depression and anoxia compared to aged controls
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The Role of Cerebellum in Alzheimer’s Disease: A Forgotten Research Corner
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Correlation of muscle strength, information processing speed and cognitive function in the elderly with cognitive impairment——evidence from EEG
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EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease
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Efficacy of preclinical pharmacological interventions against alterations of neuronal network oscillations in Alzheimer's disease: A systematic review
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Brain Networks Modulation in Young and Old Subjects During Transcranial Direct Current Stimulation Applied on Prefrontal and Parietal Cortex
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Aging and brain connectivity via electroencephalographic recordings
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Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data
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An EEG-based framework for automated discrimination of conversion to Alzheimer’s disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study
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Multilayer-Aggregation Functional Network for Identifying Brain Fatigue and Diseases
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A Preliminary Report of Network Electroencephalographic Measures in Primary Progressive Apraxia of Speech and Aphasia
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Electroencephalography‐based signatures of cognitive resilience in individuals with stable mild cognitive impairment despite carrying a high‐risk for progression to dementia
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Altered cortical functional networks in Wilson's Disease: A resting-state electroencephalogram study
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Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review
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Derivation of an intrinsic brain activity biomarker for the earliest prediction of cognitive decline
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Ridge‐penalized adaptive Mantel test and its application in imaging genetics
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Distributed Functional Connectome of White Matter in Patients With Functional Dyspepsia
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Commentary on Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer’s Disease from Mild Cognitive Impairment
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Bayesian reduced rank regression reveals generalisable neural fingerprints that differentiate between individuals in magnetoencephalography dataPreprint
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EEG biomarkers can predict early-stage Alzheimer’s disease and correlate with intracerebral pathology: a multimodal machine learning study
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Eyes-state-dependent alterations of magnetoencephalographic connectivity associated with delayed recall in Alzheimer’s disease via graph theory approach
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Supervised machine learning in psychiatry
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Topological study of the brain functional organization at the early stages of Alzheimer's disease using electroencephalography
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Characterization and Features of Neural Oscillations in Mental Disorders
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Towards An Integrative and Interpretable Framework to Unveil the Neurophysiological Fingerprint of Alzheimer's Disease and Mild Cognitive Impairment: A Machine Learning-SHAP ApproachPreprint
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Characterization and Features of Neural Oscillations in Mental Disorders
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Additional file 1 of Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology
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Predicting progression from amnestic mild cognitive impairment to Alzheimer's disease using longitudinal EEG data: a 12-month cohort study
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Self-citation

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment
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Self-citation
Differentiating amnestic from non-amnestic mild cognitive impairment subtypes using graph theoretical measures of electroencephalography
Kim, J.-G., Kim, H., Hwang, J., Kang, S. H., Lee, C., Woo, J., Kim, C., Han, K., Kim, J. B., & Park, K. W. (2022) · Scientific Reports · doi:10.1038/s41598-022-10322-9 · Cited by 23 · see citing works

Authors: Vecchio, F., Miraglia, F., Iberite, F., Lacidogna, G., Guglielmi, V., Marra, C., Pasqualetti, P., Tiziano, F. D., & Rossini, P. M.
Affiliations: IRCCS San Raffaele Roma (IT)
Università Cattolica del Sacro Cuore (IT)
Fatebenefratelli Hospital (IT)
Agostino Gemelli University Polyclinic (IT)
Corresponding author: Rossini, Paolo Maria
ORCID: Vecchio, Fabrizio
Miraglia, Francesca
Iberite, Francesco
Lacidogna, Giordano
Guglielmi, Valeria
Marra, Camillo
Pasqualetti, Patrizio
Tiziano, Francesco Danilo
Rossini, Paolo Maria
Venue: Annals of Neurology
Publisher: Wiley
Published: 2018-08
Volume: 84
Issue: 2
Pages: 302-314
ISSN: 0364-5134 (ISSN-L)
1531-8249
DOI: 10.1002/ana.25289
OpenAlex: W2883616350
Semantic Scholar: f2a210c50e217d4c9307e1126e3400dbe2c34509
Cited by: 89
References: 66
Citations by year: 2026: 6 · 2025: 13 · 2024: 8 · 2023: 11 · 2022: 15 · 2021: 15 · 2020: 13 · 2019: 8
FWCI: 7.03
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 24 d)
Locations: Annals of Neurology (publishedVersion)
PubMed (publishedVersion)
IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by-nc-nd)
Funders: Ministry of Health, Italy
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: mild cognitive impairment, Alzheimer's disease, dementia prediction, EEG connectivity, graph theory, APOE genotype, genetic risk factors, MCI conversion, EEG frequency bands, power spectral density
MeSH terms: Aged, Alzheimer Disease, Apolipoproteins E, Brain, Electroencephalography, Female, Follow-Up Studies, Genetic Markers, Humans, Male, Nerve Net, Predictive Value of Tests, Disease Progression, Cognitive Dysfunction

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Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E

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